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Nurettin Acir - One of the best experts on this subject based on the ideXlab platform.

  • A complex-valued adaptive Filter Algorithm for system identification problem
    2015 9th International Conference on Electrical and Electronics Engineering (ELECO), 2015
    Co-Authors: Engin Cemal Menguc, Nurettin Acir
    Abstract:

    In this study, a complex-valued adaptive Filter Algorithm based on Lyapunov stability theory is presented to solve a system identification problem in the complex domain. The performance of the proposed complex-valued Lyapunov adaptive Filter (CLAF) Algorithm is improved for the complex-valued system identification problem by integrating the LST into the Filter optimization cost. The performance of the proposed Algorithm is tested on a complex-valued moving average (MA) system identification problem and compared with the conventional complex-valued least mean square (CLMS) and complex-valued normalized least mean square (CNLMS) Algorithms. The simulation results show that the proposed CLAF Algorithm has achieved a faster convergence rate and a lower steady-state MSE performance when compared to the other Algorithms.

  • lyapunov stability theory based complex valued adaptive Filter design
    Signal Processing and Communications Applications Conference, 2014
    Co-Authors: Engin Cemal Menguc, Nurettin Acir
    Abstract:

    In this study, a novel complex valued adaptive Filter Algorithm is proposed satisfying stability in the sense of Lyapunov. The prediction capability of the proposed Algorithm is presented by using complex valued autoregressive process and wind signal in the literature. The proposed complex valued adaptive Filter Algorithm is compared with standard complex normalized least mean square Algorithm and performed in a high performance.

  • lyapunov stability theory based adaptive Filter Algorithm for noisy measurements
    International Conference on Computer Modelling and Simulation, 2013
    Co-Authors: Engin Cemal Menguc, Nurettin Acir
    Abstract:

    This paper presents a Lyapunov stability theory based adaptive Filter Algorithm with a determined step size. The proposed Algorithm thanks to its step size leads to a faster convergence rate and a lover misadjustment error in case of the noisy measurement environments. Also the proposed Algorithm ensures to estimate the best optimal unknown weight vector by using a step size. Simulations on white and non-white Gaussian input signals justify the proposed Algorithm for the noisy environments. The simulation results demonstrate good tracking capability and low misalignment error of the proposed Algorithm in case of the noisy measurement environments for system identification problems.

  • UKSim - Lyapunov Stability Theory Based Adaptive Filter Algorithm for Noisy Measurements
    2013 UKSim 15th International Conference on Computer Modelling and Simulation, 2013
    Co-Authors: Engin Cemal Menguc, Nurettin Acir
    Abstract:

    This paper presents a Lyapunov stability theory based adaptive Filter Algorithm with a determined step size. The proposed Algorithm thanks to its step size leads to a faster convergence rate and a lover misadjustment error in case of the noisy measurement environments. Also the proposed Algorithm ensures to estimate the best optimal unknown weight vector by using a step size. Simulations on white and non-white Gaussian input signals justify the proposed Algorithm for the noisy environments. The simulation results demonstrate good tracking capability and low misalignment error of the proposed Algorithm in case of the noisy measurement environments for system identification problems.

Engin Cemal Menguc - One of the best experts on this subject based on the ideXlab platform.

  • A complex-valued adaptive Filter Algorithm for system identification problem
    2015 9th International Conference on Electrical and Electronics Engineering (ELECO), 2015
    Co-Authors: Engin Cemal Menguc, Nurettin Acir
    Abstract:

    In this study, a complex-valued adaptive Filter Algorithm based on Lyapunov stability theory is presented to solve a system identification problem in the complex domain. The performance of the proposed complex-valued Lyapunov adaptive Filter (CLAF) Algorithm is improved for the complex-valued system identification problem by integrating the LST into the Filter optimization cost. The performance of the proposed Algorithm is tested on a complex-valued moving average (MA) system identification problem and compared with the conventional complex-valued least mean square (CLMS) and complex-valued normalized least mean square (CNLMS) Algorithms. The simulation results show that the proposed CLAF Algorithm has achieved a faster convergence rate and a lower steady-state MSE performance when compared to the other Algorithms.

  • lyapunov stability theory based complex valued adaptive Filter design
    Signal Processing and Communications Applications Conference, 2014
    Co-Authors: Engin Cemal Menguc, Nurettin Acir
    Abstract:

    In this study, a novel complex valued adaptive Filter Algorithm is proposed satisfying stability in the sense of Lyapunov. The prediction capability of the proposed Algorithm is presented by using complex valued autoregressive process and wind signal in the literature. The proposed complex valued adaptive Filter Algorithm is compared with standard complex normalized least mean square Algorithm and performed in a high performance.

  • lyapunov stability theory based adaptive Filter Algorithm for noisy measurements
    International Conference on Computer Modelling and Simulation, 2013
    Co-Authors: Engin Cemal Menguc, Nurettin Acir
    Abstract:

    This paper presents a Lyapunov stability theory based adaptive Filter Algorithm with a determined step size. The proposed Algorithm thanks to its step size leads to a faster convergence rate and a lover misadjustment error in case of the noisy measurement environments. Also the proposed Algorithm ensures to estimate the best optimal unknown weight vector by using a step size. Simulations on white and non-white Gaussian input signals justify the proposed Algorithm for the noisy environments. The simulation results demonstrate good tracking capability and low misalignment error of the proposed Algorithm in case of the noisy measurement environments for system identification problems.

  • UKSim - Lyapunov Stability Theory Based Adaptive Filter Algorithm for Noisy Measurements
    2013 UKSim 15th International Conference on Computer Modelling and Simulation, 2013
    Co-Authors: Engin Cemal Menguc, Nurettin Acir
    Abstract:

    This paper presents a Lyapunov stability theory based adaptive Filter Algorithm with a determined step size. The proposed Algorithm thanks to its step size leads to a faster convergence rate and a lover misadjustment error in case of the noisy measurement environments. Also the proposed Algorithm ensures to estimate the best optimal unknown weight vector by using a step size. Simulations on white and non-white Gaussian input signals justify the proposed Algorithm for the noisy environments. The simulation results demonstrate good tracking capability and low misalignment error of the proposed Algorithm in case of the noisy measurement environments for system identification problems.

Nurettin Acr - One of the best experts on this subject based on the ideXlab platform.

  • An augmented complex-valued Lyapunov stability theory based adaptive Filter Algorithm
    Signal Processing, 2017
    Co-Authors: Engin Cemal Meng, Nurettin Acr
    Abstract:

    A novel Algorithm (ACLAF) is derived by using the LST and augmented statistics.The ACLAF Algorithm provides a significant performance gain in noncircular signals.Rigorous analyses of the ACLAF Algorithm are presented.Its performance is verified on both benchmark and real-world data. A novel augmented complex-valued Lyapunov stability theory (LST) based adaptive Filter (ACLAF) Algorithm is proposed for the widely linear adaptive Filtering of noncircular complex-valued signals. After a candidate Lyapunov function is determined, the design procedure is formulated as an inequality constrained optimization problem by using augmented statistics and LST. Thus, the proposed Algorithm has improved the adaptive Filtering of noncircular complex-valued signals by a unified framework of the LST and augmented complex statistics. Moreover, we statistically show that the ACLAF Algorithm converges to the optimal Wiener solution under stationary environments, the required condition of the step size for the stability of the ACLAF Algorithm is obtained by convergence in mean analysis and a new approach. In addition, the variance of the ACLAF Algorithm is statically analysed in this study. The performance of the ACLAF Algorithm is tested on circular and noncircular benchmark signals and on a real-world noncircular wind signal. Simulation results verify that the ACLAF Algorithm outperforms complex-valued LST based adaptive Filter (CLAF), complex-valued least mean square (CLMS), complex-valued normalized least mean square (CNLMS), augmented CLMS (ACLMS) and augmented CNLMS (ACNLMS) Algorithms for adaptive prediction of noncircular signals in terms of prediction gain, convergence rate and mean square error (MSE). Also, the ACLAF Algorithm enhances the prediction gain by more than 25% when compared to the other augmented Algorithms.

Xia Chuan-hao - One of the best experts on this subject based on the ideXlab platform.

  • Adaptive α-β Filter Algorithm
    Journal of Computer Applications, 2007
    Co-Authors: Xia Chuan-hao
    Abstract:

    Data processing system is the key of phased array radar search and multi-target tracking,and the tracking Filter Algorithm will directly determine the merits of the system's performance.Adaptive α-β Filter Algorithm was introduced,and then it was compared to LSR Filter Algorithm and Kalman Filter Algorithm,the mean square deviation of distance,position,speed and direction of these Algorithms on the flight line was analyzed to discuss the specific use of the three Filters in straight-line flight and about-ship straight-line flight.The experimental results prove that the adaptiveα-β Filter Algorithm has good effect in straight-line with small computational complexity,and it is in favor of modeling and simulation.

Yang Shujun - One of the best experts on this subject based on the ideXlab platform.

  • Weights Optimization Particle Filter Algorithm in Multi-sensor Measurement
    Computer Science, 2013
    Co-Authors: Hu Zhentao, Yang Shujun
    Abstract:

    Aiming at the effective realization of particle Filter in multi-sensor measurement system state estimation,a novel particle Filter Algorithm based on weights optimization in multi-sensor measurement was proposed in this paper.In the new Algorithm,the measurements likelihood function is firstly constructed on the basis of the concrete form of proposal distribution,and all measurement in single Filter period are used to calculate the every particle weights,respectively.Secondly,given the otherness of different sensors precision,combining with priori information of sensors precision, the weighting fusion method is used to optimize every particle weights in multi-sensor measurement.Finally,the Filter precision is improved by decreasing the variance of particle weights.The theoretical analysis and experimental results show the feasibility and efficiency of the proposed Algorithm.